Evidence map›Paper›PMID 35524889›Full record

ArticleEnvironmental monitoring and assessment2022

Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan.

Rehan Khan, Huan Li, Muhammad Basir, Yuan Lin Chen, Meer Muhammad Sajjad, Ihtisham Ul Haq, Barkat Ullah, Muhammad Arif, Waqas Hassan

Abstract read
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In one paragraph

Article in Environmental monitoring and assessment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Rehan KhanKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China.
Huan LiKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China. lihuan@csu.edu.cn.ORCID http://orcid.org/0000-0001-5211-8324
Muhammad BasirKey State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China.
Yuan Lin ChenKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China.
Meer Muhammad SajjadUniversity of Chinese Academy of Science (UCAS), Beijing, 101408, China.
Ihtisham Ul HaqKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China.
Barkat UllahSchool of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
Muhammad ArifDepartment of Computation and Investigation Center Institute of Polytechnic National, 77500, Mexico City, Mexico.
Waqas HassanNational Engineering Research Center for Geographic Information System (NERCGIS) School of Geography, China University of Geosciences, Wuhan, 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Land use/land cover (LULC) changes due to urban growth on the regional scale affect land surface temperature (LST). The present study aims to assess the LULC changes and their impact on LST over Mardan and Charsadda districts of Khyber Pakhtunkhwa (KP), Pakistan, in the period from 1990 to 2019. Landsat satellite (TM& ETM +) datasets in the period from 1990 to 2010 and Sentinel-2 images from 2016 to 2019 were used in this study. All the datasets were pre-processed and the LULC types were classified by maximum likelihood classification algorithm. The vegetation degradation was computed from normalized difference vegetation index (NDVI), and the LST was derived based on the LULC changes. The results showed that the overall accuracy of LULC classification was 87.84%. Dramatic LULC changes were observed during the last three decades, where the vegetation degradation area was decreased from 1307.8 (59.27%) to 1147.6 km

Indexed as

Environmental MonitoringUrban RenewalPakistanTemperatureUrbanizationGISImage classificationLand cover changesLand surface temperatureRemote sensingVegetation

Identifiers

PMID35524889

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.